Better Data for Better Agricultural Policies: The Living Standards Measurement Study Integrated Surveys on Agriculture Gero Carletto and Alberto Zezza Development Research Group World Bank Ravello June 18, 2013

Background and overview on LSMS and LSMS-ISA Selected program highlights, innovations – Gender – Use of technology in surveys – Geospatial data Policy and methods research Some final considerations Outline

The LSMS over time Est Evolution … – Poverty monitoring and measurement: the “McNamara anecdote” – Technical assistance, capacity building – Back to the “roots”: strong research agenda (methodological and policy) – Focus on agriculture, and on Africa: LSMS- ISA

The LSMS ‘philosophy’ Need to understand living standards, and the correlates and determinants not just monitor… >>>>

The LSMS ‘philosophy’ Need to understand living standards, and the correlates and determinants not just monitor… the sum is greater than the parts! Demand driven, country owned, capacity Priority given to meeting the policy needs of each country, but an eye to x-country comparability Strict quality control Dissemination, open data

Ag statistics (in Africa): dismal state of affairs Data on households engaged in agriculture suffer on many fronts Quality: Practices, technologies Availability, dissemination, documentation Policy relevance Inconsistency, lack of integration and coordination

Your many averages of maize yields in your avg. country

… not to mention cassava!

Lack of policy relevance Failure to: Integrate – Link with external data on ecosystems – Look at livelihoods (farmers do more than farming) – Collect with data on poverty, health, other human development measures – Capture gender & other within household aspects of farming Measure dynamics/transitions

The LSMS – ISA Project Collecting household survey data with focus on agriculture in 7+ SSA countries Motivation: Dismal availability, quality and relevance of ag stats in Africa Building capacity in national institutions Improving methodologies in agricultural statistics, producing best practice guidelines & research Documenting & disseminating micro data, policy research

Main Features 6+ year program ( ) 7 Sub-Saharan African countries Panel Sample: 3-5,000 households – Population-based frame – Representative at national- and few sub- national levels Tracking: Movers, Subsample of split-offs Open data access policy – Micro-data publicly available within 12 months of data collection

Schedule of surveys CountryBaselineAdditional waves Tanzania2008/092010/112012/132014/15 Uganda2009/102010/112011/122013/14 Malawi2010/ Nigeria2010/112012/13 Ethiopia2011/122013/14 Niger Burkina Faso2014 Mali2014

Main Features (cont’d) Gender-disaggregated data Use of technology – GPS for households and plots (area) GPS for households and plots (area) – Concurrent field-based data entry Concurrent field-based data entry – Computer Assisted Personal Interviews (CAPI) – Integration via Geo-referencing (links to other data sources) Integration

Our research agenda: Policy and Methods Policy: Gender Differentials in Productivity Farm Household Production and Nutritional Outcomes” Fact and Myths in African Agriculture Anno 2012 Methods: Productivity measurement (inputs, outputs) Technology adoption Gender …

Take home messages: The PhD perspective? Agenda still huge – Data availability – Methods/Tools/Technologies – Analytical work Open data: A gold mine for theses, and post- docs… An employment opportunity?

Better Data for Better Agricultural Policies: The Living Standards Measurement Study Integrated Surveys on Agriculture Gero Carletto and Alberto Zezza Development Research Group World Bank

Surveys: Going Beyond Rates Understanding secondary school enrollments, year olds, Albania 2002 Average Percent In almost all countries we have a single statistic: mean enrollment at the national level. In this case it is 61%. This is interesting for monitoring purposes, but it doesn’t say much about poverty or other factors.... A regional disaggregation would be useful

Understanding secondary school enrollments, year olds, Albania 2002 Average Percent In some countries we have regional breakdowns, with marked contrasts The contrast between urban and rural rates emphasizes the disadvantages faced by rural communities. What other breakdown would be useful? Urban Rural

Understanding secondary school enrollments, year olds, Albania 2002 Average Percent …with luck, official statistics can add the gender dimension …the figures show that, in urban areas, there is no gender differential but a large gap in rural areas. But we still don’t know much about who sends their children to school Urban Rural Male Female Male Female

Understanding secondary school enrollments, year olds, Albania Percent …With a survey we can show enrollment rates broken down by consumption level- -and thus understand an additional dimension >> Consumption quintile Female, urban Male, urban Male, rural Female, urban Average >>

Is women’s control of income important for child nutrition? Dependent Variable: Z-Score of Height-for-Age Definitions of Woman’s Share of Household Income V1 Assumption 100 to Head V2 Assumption 50/50 Split V3 Assumption a la HH V4 Preferred Child: Male ** Woman's Share of Household Income x Male Child *** Observations2,522 R note: *** p<0.01, ** p<0.05, * p<0.1 >>

Everyone rounds up…

Source: Carletto, Savastano, Zezza (2013). “Fact or Artifact: the Impact of Measurement Errors on the Farm size - Productivity Relationship”, Journal of Development Economics. …large farmers under report…

The IR is strengthened if we use GPS! >>>>

Concurrent Data Entry The case of missing plot measurements High initial rates of missing gps data in months 1 & 2

Concurrent Date Entry ( cont’d) The case of missing plot measurements Intervention - High rate of missing data observed and new instructions to field disseminated.

Concurrent Data Entry The case of missing plot measurements >> Substantial decrease in missing data. Because of revisit of households in month 4-6, part of the missing data was now captured.

Data to understand inter-relationships between agriculture & behavior – How does variability in climate affect productivity? What are the indirect effects on nutrition, health, human capital development? – How does distance to market affect value of farm product? And off-farm work opportunities? – How does length of crop season affect productivity and seasonality of wellbeing, hunger, children? Integrate space, agro-ecology into ag micro-economics

What we do Record household and plot locations with GPS – Protocol to avoid releasing this information as it would violate confidentiality

ThemeVariable DistancePlot distance to household Household distance to paved road Household distance to major market (if available) ClimatologyAnnual mean temperature Mean temperature of wettest quarter Mean temperature of driest quarter Annual precipitation Precipitation of wettest quarter Precipitation of driest quarter Precipitation seasonality (coefficient of variation) LandscapeLand cover class TypologyAgro-ecological zone Elevation Slope class Topographic wetness index Landscape-level soil characteristics Time series,Short-term average crop season rainfall total crop seasonSpecific crop season rainfall total Short-term average NDVI crop season aggregates Specific crop season NDVI crop season aggregates Geo-spatial variables describing physical environment, mostly using public domain data sources (NASA, NOAA, AfSIS, ISRIC..) Focus on factors affecting agricultural productivity: ⎻Distance ⎻Climatology ⎻Landscape Typology ⎻Time series Integrate geo-spatial data

Coverage of African Drylands (descriptive)

Household Distance to Major Road (km) Remoteness negatively affects household-level agricultural productivity & incomes Analysis of household data on the effects of road connectivity on input use, crop output, and household incomes in Madagascar and Ethiopia (Chamberlin and others 2007; Stifel and Minten 2008) Distance

Average Annual Rainfall (mm)Average Annual Temperature (°C) Climatology

Elevation (m) Topography can have a significant influence on yields Elevation and derivatives (slope, relief roughness, topographic wetness index) affect water availability, soil fertility, land degradation & management requirements Landscape typology

Rainfall (mm) Rainfall time series 2010 Rainfall as % of Normal

Vegetation time series >>>> 2010 Max EVI Deviation from Mean sparsedense moderate NDVI sparsedense moderate